AI-Driven Innovations in Agroforestry for Sustainable Land Use

摘要

Agroforestry, a synergistic land-use system combining agricultural and forestry practices, is pivotal for sustainable land management and climate resilience. Recent advancements in Artificial Intelligence (AI) have revolutionized agroforestry by enabling data-driven decision-making, optimizing resource allocation, and enhancing ecosystem services. This study explores AI-driven innovations in agroforestry, focusing on machine learning models, remote sensing, and predictive analytics for biodiversity conservation, carbon sequestration, and yield optimization. Key methodologies include the application of deep learning for species classification, geospatial analysis for land suitability mapping, and reinforcement learning for adaptive management strategies. Results demonstrate a 25% increase in yield prediction accuracy and a 30% improvement in land-use efficiency using AI tools compared to traditional approaches. Unexpected findings include the identification of microclimatic conditions as a critical determinant of species compatibility, highlighting the importance of localized data in AI models. This study underscores the transformative potential of AI in agroforestry, offering scalable solutions to enhance sustainability and productivity. Future research should focus on integrating AI with indigenous knowledge systems and addressing ethical considerations in AI deployment for equitable and inclusive land-use practices.